Identifying geothermal steam spots based on binary logistic method to a high resolution imagery and field measurement

Abstract

The increasing population of a country has an impact on the number of energy needs that must be provided. Therefore, alternative sources of energy need to be developed to meet those needs. Indonesia is known as The Ring of Fire has very high geothermal resource potential. A study of geothermal mapping technology needs to be done to optimize the utilization of this energy. This study aims to recognize the relation between temperature and surface roughness on an up-flow zone of geothermal system and identify the steam spots at surface. We used a high resolution ALOS-2 PALSAR-2 images to obtain surface roughness and ASTER images to obtain a corrected Land Surface Temperature (LST). The study was conducted in an open area Papandayan Crater, Garut, West Java (Indonesia). Data pre-processing including atmospheric and geometric corrections were applied following an image processing to obtain the surface roughness and the corrected LST by estimating non-unique surface emissivity, pixel fitting between measured surface temperature and roughness at field, and identifying the steam spots of the geothermal system using binary logistic regression method. Equally good models of co-polarization and cross-polarization obtained because of the high correlation between co-polarization and cross-polarization surface roughness. There was a positive relationship between land surface temperature and surface roughness in the manifestation area of geothermal resources and surface roughness known more dominant in determining the manifestations of geothermal resources than surface temperature.